Hands-on_Ex03

Import package

Interactive Data Visualisation

Import data

exam_data <- read.csv("data/Exam_data.csv")

Interactive graph with ggiraph,tooltip

Tooltip effect with tooltip aesthetic

1. Tooltip: show label when mouse scroll on it
2. histodot: dot stacks vertically
3. svg: Scalable Vector Graphics(adjust size so can with good resolution) for html page
4. ratio of 0.618 is a recommended ratio for visually pleasing

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(aes(tooltip=ID),
                           stackgroups=TRUE,
                           binwidth=1,
                           method='histodot')+
    scale_y_continuous(NULL,breaks=NULL)
girafe(
  ggobj=plot,
  width_svg = 6,
  height_svg = 6*0.618
)

Displaying multiple information

1. Use “$” to extract specific column from dataframe
2. “paste0” to concatenate string

exam_data$tooltip <- c(paste0(
  "Name =", exam_data$ID, "\n Class =", exam_data$CLASS
))
plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(aes(tooltip=exam_data$tooltip),
                           stackgroups=TRUE,
                           binwidth=1,
                           method='histodot')+
    scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg = 8,
  height_svg = 8*0.618
)

Warning: Use of `exam_data$tooltip` is discouraged.
ℹ Use `tooltip` instead.

Customising Tooltip

Use opts_tooltip() to customize interactive label s font and color

tooltip_css <- "background-color:white;
font-style:bold; color:blue;"

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(aes(tooltip=ID),
                           stackgroups=TRUE,
                           binwidth=1,
                           method="histodot")+
  scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg = 6,
  height_svg = 6*0.618,
  options = list(
    opts_tooltip(
      css=tooltip_css))
)

Displaying statistics

1. accuracy: 0.01 means to round to two decimal places

2. ymax: upper limit of 90% interval of y, y: average

3. scales::number is to format value with accuracy (0.01)

4. after_stat:tooltip applied after calculating statistic values

5. fun.data: summary statistics, mean_se:calculate mean and standard error

tooltip <- function(y,ymax,accuracy=0.001){
  mean <- scales::number(y,accuracy=accuracy)
  se <- scales::number(ymax-y,accuracy = accuracy)
  paste0("Mean math scores",mean," +/- ",se)
}

gg_point <- ggplot(data=exam_data,aes(x=RACE)
               )+
  stat_summary(aes(y=MATHS,
                   tooltip=after_stat(
                     tooltip(y,ymax))),
  fun.data="mean_se",
  geom=GeomInteractiveCol,
  fill="lightblue"
    )+
  stat_summary(aes(y=MATHS),
  fun.data="mean_se",
  geom="errorbar",width=0.2,size=0.2)

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

girafe(
  ggobj=gg_point,
  width_svg = 8,
  height_svg = 8*0.6188)

Highlight effect

data_id allows highlighting effect on dotplot with defined category, default setting is filling with orange color.

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(aes(data_id=CLASS),
                           stackgroups=TRUE,
                           binwidth=1,
                           method="histodot")+
  scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg = 6,
  height_svg=6*0.618
)

Changing hovering color setting

opacity: changing the transparency, opacity=0.2 means with 80% transparency

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(aes(data_id=CLASS),
                           stackgroups=TRUE,
                           binwidth=1,
                           method="histodot")+
  scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg = 6,
  height_svg=6*0.618,
  options = list(
    opts_hover(css="fill: #202020;"),
    opts_hover_inv(css="opacity:0.1;")
    
  )
)

Combine tooltip(label) and hovering effect

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(
    aes(tooltip=CLASS,data_id=CLASS),
    stackgroups=TRUE,
    binwidth=2,
    method="histodot"
  )+
  scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg=6,
  height_svg=6*0.618,
  options = list(
    opts_hover(css="fill:#202020;"),
    opts_hover_inv(css="opacity:0.2;")
  )
  
)

Insert link on the graph

1. add onclick column to exam_data
2. sprintf: combine new string with URL
3. “%s%s”, first %s is the link, second %s is new adding string
4. as.character: convert to character(string) type, and adding to URL

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
 "https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
 as.character(exam_data$ID))

plot <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(
    aes(onclick=onclick),
    stackgroups=TRUE,
    binwidth=2,
    method="histodot"
  )+
  scale_y_continuous(NULL,breaks=NULL)

girafe(
  ggobj=plot,
  width_svg=6,
  height_svg=6*0.618
  
)

Coordinate multiple views

1. opts_hover: setting about hovering point
2. opts_hover_inv: setting for other part when hovering

plot1 <- ggplot(data=exam_data,aes(x=MATHS))+
  geom_dotplot_interactive(
    aes(data_id=ID),
    stackgroups=TRUE,
    binwidth=2,
    method="histodot"
  )+
  coord_cartesian(xlim=c(0,100))+
  scale_y_continuous(NULL,breaks=NULL)

plot2 <- ggplot(data=exam_data,aes(x=ENGLISH))+
  geom_dotplot_interactive(
    aes(data_id=ID),
    stackgroups=TRUE,
    binwidth=2,
    method="histodot"
  )+
  coord_cartesian(xlim=c(0,100))+
  scale_y_continuous(NULL,breaks=NULL)

girafe(code = print(plot1+plot2),
  width_svg=6,
  height_svg=3,
  options=list(
    opts_hover(css="fill: #202020;"),
    opts_hover_inv(css="opacity:0.2;")
  
))

Interactive graphs by plotly

Interactive scatter plot

#|message: false
plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Adding visual effect

plot_ly(data= exam_data,
       x= ~MATHS,
       y= ~ENGLISH,
       color= ~RACE)

Coordinate multiple views

1. highlight_key: create shared data for two plots
2. subplot: coordinate two plots together

d <- highlight_key(exam_data)

p1 <- ggplot(data=d,aes(x=MATHS,y=ENGLISH))+
  geom_point(size=2)+
  coord_cartesian(xlim=c(0,100),ylim=c(0,100))

p2 <- ggplot(data=d,aes(x=MATHS,y=SCIENCE))+
  geom_point(size=2)+
  coord_cartesian(xlim=c(0,100),ylim=c(0,100))

subplot(ggplotly(p1),ggplotly(p2))

Interactive graphs by ggplotly

plot <- ggplot(data=exam_data,aes(x=MATHS,y=ENGLISH))+
  geom_point(size=2)+
  coord_cartesian(xlim=c(0,100),ylim=c(0,100))


ggplotly(plot)

Data table

class=“compact” means to have compact layout for the table

DT::datatable(exam_data,class="compact")

Link table with graph

1. highlight_key: assign identifier to dataframe

2. plotly_selected: track corresponding point when mouse click on table row

3. crosstalk: combine table and graph

4. bscols: create layout with two elements

data <- highlight_key(exam_data)

p <- ggplot(data,aes(ENGLISH,MATHS))+
  geom_point(siz=1)+
  coord_cartesian(xlim=c(0,100),ylim=c(0,100))

Warning in geom_point(siz = 1): Ignoring unknown parameters: `siz`

gg <- highlight(ggplotly(p),"plotly_selected")

crosstalk::bscols(gg,DT::datatable(data),widths = 5)

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Animated Statistical Graphics

Import data

1. mutate_each or mutate(across())：apply given function to selected columns
2. %>% pass function/results to next step
3. funs all all_of: create a list of function
4. factor: turn column into categorical variable

col <- c("Country","Continent")
global <- read_xls("data/GlobalPopulation.xls",
                   sheet="Data") %>% 
  mutate(across(all_of(col), factor)) %>% 
  mutate(Year=as.integer(Year))

Population bubble plot

static plot

ggplot(global,aes(x=Old,y=Young,size=Population,colour=Country))+
  geom_point(alpha=0.7,show.legend = FALSE)+
  scale_color_manual(values=country_colors)+
  scale_size(range=c(2,10))+
  labs(title='Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young')

Animated plot with gganimate

ease_aes(): the moving way of plot element, default is linear

ggplot(global,aes(x=Old,y=Young,size=Population,colour=Country))+
  geom_point(alpha=0.7,show.legend = FALSE)+
  scale_color_manual(values=country_colors)+
  scale_size(range=c(2,10))+
  labs(title='Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young')+
  transition_time(Year)+
  ease_aes("linear")

Animated plot with ggplotly

plot <- ggplot(global,aes(x=Old,y=Young,size=Population,colour=Country))+
  geom_point(aes(size=Population, frame=Year),
             alpha=0.7,show.legend = FALSE)+
  scale_color_manual(values=country_colors)+
  scale_size(range=c(2,10))+
  labs( x = '% Aged', 
       y = '% Young')

Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame

ggplotly(plot)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

Using plot_ly()

bp <- global %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent, 
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          )
bp